code: def __init__(self, args: dict):
      BaseEncoder.__init__(self)
      self.input_dim = args.input_dim
      self.latent_dim = args.latent_dim
  
      layers = nn.ModuleList()
  
      layers.append(
          nn.Sequential(
              GatedDense(np.prod(args.input_dim), 300),
              GatedDense(300, 300),
          )
      )
  
      self.layers = layers
      self.depth = len(layers)
  
      self.embedding = nn.Linear(300, self.latent_dim)
      self.log_var = NonLinear(
          300,
          self.latent_dim,
          activation=nn.Hardtanh(min_val=-6.0, max_val=2.0),
      )
  
      for m in self.modules():
          if isinstance(m, nn.Linear):
              he_init(m)
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import VAMP, AutoModel, VAMPConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: BaseEncoder with example: obj = Encoder(args)